Data skipping and compression through partitioning of data

ABSTRACT

Conventionally, in addition to indexing, a synopsis of a base table of a database is used to skip and compress data. However, scanning of the entire synopsis for all queries is required, which takes a long time when the synopsis gets significantly big in a large data warehouse. A method for efficient data skipping and compression through vertical partitioning of data is provided to eliminate the cost of synopsis storage overhead while enabling the synopsis search functionality.

FIELD OF INVENTION

The present invention relates generally to the field of data management,and more particularly to skipping and compression of data.

BACKGROUND

Data skipping skips unnecessary processing of irrelevant or duplicatedata, loading only the information that needs to be analyzed.Specifically, data skipping refers to scanning a column (or table) tofind rows that qualify for a given query and skip over data that doesn'tqualify for your query. When skipping irrelevant data, the irrelevantdata is not read into memory from disk and CPU resources are notdemanded to find out why those irrelevant rows/columns are not needed inthe first place.

Data compression involves encoding information using fewer bits than theoriginal representation. Compression can be either lossy or lossless.Lossless compression reduces bits by identifying and eliminatingstatistical redundancy. No information is lost in lossless compression.Lossy compression reduces bits by identifying unnecessary informationand removing it. Compression is useful as it helps reduce resourceusage, such as data storage space or transmission capacity.

SUMMARY

According to an aspect of the present invention, there is a method,computer program product and/or system for data skipping andcompression, that performs the following operations (not necessarily inthe following order): partitioning projection of each data value of aset of data values into a plurality of data types, wherein the datetypes include numerical and/or comparable bytes value; and storing theplurality of data types in a set of separate columns, wherein there is aseparate column for each data type; wherein: at least the step ofstoring the plurality of data types is performed by computer softwarerunning on computer hardware.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 depicts a cloud computing node used in a first embodiment of asystem according to the present invention;

FIG. 2 depicts an embodiment of a cloud computing environment (alsocalled the “first embodiment system”) according to the presentinvention;

FIG. 3 depicts abstraction model layers used in the first embodimentsystem;

FIG. 4 is a schematic view of a second embodiment of a system accordingto the present invention;

FIG. 5 is a flowchart showing a method performed, at least in part, bythe second embodiment system; and

FIG. 6 is a schematic view of a machine logic (for example, software)portion of the second embodiment system.

DETAILED DESCRIPTION

The present invention provides a method for efficient data skipping andcompression through vertical byte partitioning of data is provided toeliminate the cost of multi-column range summaries storage overheadwhile enabling the multi-column range summaries search functionality.Data (each data attribute or a set of correlated data attributes) isre-organized and vertically partitioned in a set of bytes columns, wherethe higher order bytes of data are first scanned prior to scanning thelower order bytes of data. The lower order bytes are visited only if thehigher order bytes satisfy the query predicate partially. The presentinvention may be a system, a method, and/or a computer program product.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium, or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network, and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers, and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network, and forwards the computer readableprogram instructions for storage in a computer readable storage mediumwithin the respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computer,or entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture, including instructions which implement aspectsof the function/act specified in the flowchart and/or block diagramblock or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus, or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions, or acts, or carry out combinations of special purposehardware and computer instructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid Elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured Service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public Cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes; RISC(Reduced Instruction Set Computer) architecture based servers; storagedevices; networks and networking components. In some embodimentssoftware components include network application server software.

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and functionality according to the present invention (seefunction block 66 a).

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Some embodiments of the present invention will now be described indetail with reference to the Figures. FIG. 4 is a functional blockdiagram illustrating various portions of networked computers system 400,in accordance with one embodiment of the present invention, including:database management (DBM) sub-system 402; client sub-systems 404, 406,408, 410, 412; database 411; communication network 414; databasemanagement computer 420; communication unit 422; processor set 424;input/output (I/O) interface set 426; memory device 428; persistentstorage device 430; display device 432; external device set 434;database management system (DBMS) programs 435; random access memory(RAM) devices 440; cache memory device 442; and program 600.

Sub-system 402 is, in many respects, representative of the variouscomputer sub-system(s) in the present invention. Accordingly, severalportions of sub-system 402 will now be discussed in the followingparagraphs.

Sub-system 402 may be a laptop computer, tablet computer, netbookcomputer, personal computer (PC), a desktop computer, a personal digitalassistant (PDA), a smart phone, or any programmable electronic devicecapable of communicating with the client sub-systems via network 414.Program 600 is a collection of machine readable instructions and/or datathat is used to create, manage, and control certain software functionsthat will be discussed in detail below.

Sub-system 402 is capable of communicating with other computersub-systems via network 414. Network 414 can be, for example, a localarea network (LAN), a wide area network (WAN) such as the Internet, or acombination of the two, and can include wired, wireless, or fiber opticconnections. In general, network 414 can be any combination ofconnections and protocols that will support communications betweenserver and client sub-systems.

Sub-system 402 is shown as a block diagram with many double arrows.These double arrows (no separate reference numerals) represent acommunications fabric, which provides communications between variouscomponents of sub-system 402. This communications fabric can beimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware component within a system. For example,the communications fabric can be implemented, at least in part, with oneor more buses.

Memory 428 and persistent storage 430 are computer readable storagemedia. In general, memory 428 can include any suitable volatile ornon-volatile computer readable storage media. It is further noted that,now and/or in the near future: (i) external device(s) 434 may be able tosupply, some or all, memory for sub-system 402; and/or (ii) devicesexternal to sub-system 402 may be able to provide memory for sub-system402.

Program 600 is stored in persistent storage 430 for access and/orexecution by one or more of the respective computer processors 204,usually through one or more memories of memory 428. Program 600 islocated within DBMS programs 435, where other programs (not shown) carryout various functions of database management. Persistent storage 430:(i) is at least more persistent than a signal in transit; (ii) storesthe program (including its soft logic and/or data), on a tangible medium(such as magnetic or optical domains); and (iii) is substantially lesspersistent than permanent storage. Alternatively, data storage may bemore persistent and/or permanent than the type of storage provided bypersistent storage 430.

Program 600 may include both machine readable and performableinstructions, and/or substantive data (that is, the type of data storedin a database). In this particular embodiment, persistent storage 430includes a magnetic hard disk drive. To name some possible variations,persistent storage 430 may include a solid state hard drive, asemiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 430 may also be removable. Forexample, a removable hard drive may be used for persistent storage 430.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage430.

Communications unit 422, in these examples, provides for communicationswith other data processing systems or devices external to sub-system402. In these examples, communications unit 422 includes one or morenetwork interface cards. Communications unit 422 may providecommunications through the use of either, or both, physical and wirelesscommunications links. Any software modules discussed herein may bedownloaded to a persistent storage device (such as persistent storagedevice 430) through a communications unit (such as communications unit422).

I/O interface set 426 allows for input and output of data with otherdevices that may be connected locally in data communication withcomputer 420. For example, I/O interface set 426 provides a connectionto external device set 434. External device set 234 will typicallyinclude devices such as a keyboard, keypad, a touch screen, and/or someother suitable input device. External device set 234 can also includeportable computer readable storage media such as, for example, thumbdrives, portable optical or magnetic disks, and memory cards. Softwareand data used to practice embodiments of the present invention, forexample, program 600, can be stored on such portable computer readablestorage media. In these embodiments the relevant software may (or maynot) be loaded, in whole or in part, onto persistent storage device 430via I/O interface set 426. I/O interface set 426 also connects in datacommunication with display device 432.

Display device 432 provides a mechanism to display data to a user andmay be, for example, a computer monitor or a smart phone display screen.

Database 411 is an organized collection of data. The data is typicallyorganized to model aspects of reality in a way that supports processesrequiring information. For example, modelling the availability of roomsin hotels in a way that supports finding a hotel with vacancies.

Program 600 operates to organize data in upper and lower bytes to unifydata and data summarization and to avoid the need for the redundant datasummarization. Further, program 600 scans and skips data blocks ofcolumns in a database (e.g., a relational database), where each columnis stored and/or sorted in upper and lower bytes and the data blocks ofcolumns are given by scanning a synopsis of a base table in thedatabase.

Some embodiments of the present invention recognize the following facts,potential problems and/or potential areas for improvement with respectto the current state of the art: (i) lots of indexes on tables areconventionally used for data skipping; (ii) every time a row is updatedindex keys are updated as well; (iii) in addition to indexing, dataskipping is primarily achieved using a synopsis (i.e., summary ofmin/max values in a data block); and/or (iv) in a real customersituations, synopsis can get significantly big in a large datawarehouse, over a billion rows and thus scanning a synopsis itself cantake a long time.

Conventionally, one of techniques to speed up analytical queries in anoperational data store/database is to identify and built many indexes.Identifying the right indexes has been a challenge in a real-worldsetting and a huge burden on database administrator. In addition,maintaining indexes increases the updates cost in transaction processing(also prevalent in operational data store) and all changes to indexesmust be logged, yet another prohibiting cost. To overcome the problemsof identifying and maintaining indexes, a paradigm for speeding upqueries is explored through multi-column range summaries on an orderedset of correlated attributes, for example, scanning and locating themost relevant data based on columns instead of rows which results infaster processing. However, these multi-column range summariesintroduces additional space and management overhead because, they mustbe stored and maintained (for insert, update, delete) in addition tomaintaining the base data.

Conventionally, one way for multi-column range summary is a summary ofmin/max values in data block, also referred to as synopsis, by whichdata skipping is primarily achieved. Synopsis are simple and easy to useand requires minimal tuning. However, Synopsis requires the scanning ofthe entire synopsis for all queries. Further, in real situations,synopsis can get really big in a large data warehouse (e.g., over abillion rows). Thus scanning synopsis itself can take a long time, andthe synopsis may requires its own “synopsis.” An example of obtaining asynopsis from a base table is shown in Tables 1 and 2 where a base tablein a database is converted to a synopsis of the base table bysummarizing the min/max values in data blocks. Herein the data blockrefers to a chunk of data or a set of records in the base table. Forexample, in Table 1, rows 1-3, which are referred to as a data block inthe base table, are grouped to reduce to one single row in the synopsisin Table 2; and the data in column B of rows 1-3 in the base table issummarized to data b₁-b₃ (min-max) in the synopsis.

TABLE 1 A base table in a database. A B C D . . . 1 a₁ b₁ c₁ d₁ 2 a₁ b₁c₃ d₅ 3 a₁ b₃ c₁ d₈ 4 a₂ b₂ c₂ d₁ 5 a₂ b₂ c₂ d₉ 6 a₃ b₃ c₁ d₂

TABLE 2 A synopsis of the base table in Table 1. A B C D . . . 1-3 a₁b₁-b₃ c₁-c₃ d₁-d₈ 4-5 a₁ b₂ c₂ d₁-d₉ 6 a₁ b₃ c₁ d₂

Some embodiments of the present invention provide a method for changingthe representation of data to create an implicit synopsis (also referredto as an embedded second-level synopsis) as opposed to a conventionalsynopsis (also referred to as an explicit synopsis). The conventionalsynopsis herein is stored explicitly and separate from the base table,as shown in Tables 1 and 2. Essentially the implicit synopsis isembedded within the base table data by changing the data representation.Thus, the storage and maintenance of the conventional synopsis data isavoided, and the cost of accessing synopsis in addition to accessing thebase table data is avoided as well. Furthermore, for correlated columns,higher/upper bytes values of a set of attributes are stored only once,which not only reduces the storage but also reduces the scanning, i.e.,the embedded synopsis is stored once.

One example is described in Table 3, 4 and 5. Table 3 shows a base tablehaving a set of columns A, B, and C, and each data value in each columnof the set of columns is divided into an upper bytes value and a lowerbytes value, such as, a₁a₂ where a₁ represents an upper bytes value anda₂ represents a lower bytes value. In Table 4, each upper bytes valueand its corresponding lower bytes value are stored separately indifferent columns, such as, a₁ is stored in column A₁ and a₂ is storedin column A₂, where column A₁ may be an implicit synopsis (i.e., anembedded synopsis in the base table) Further in Table 5, for a set ofcorrelated columns, such as, columns A and C in Table 3, which have sameupper bytes values (i.e., data values in columns A₁ and C₁ in Table 4are the same), the same upper bytes values are stored only once.

TABLE 3 A base table having data values that are divided into an upperbytes value and a lower bytes value. A B C . . . 1 a₁a₁ b₁b₁ a₁c₁ 2 a₁a₂b₁b₂ a₁c₃ 3 a₁a₃ b₂b₁ a₁c₁ 4 a₂a₁ b₂b₂ a₂c₂ 5 a₂a₂ b₂b₃ a₂c₂ 6 a₃a₁ b₂b₄a₃c₁

TABLE 4 The base table in Table 3 in which the upper bytes value and itscorresponding lower bytes value are stored separately in differentcolumns. A₁ A₂ B₁ B₂ C₁ C₂ . . . 1 a₁ a₁ b₁ b₁ a₁ c₁ 2 a₁ a₂ b₁ b₂ a₁ c₃3 a₁ a₃ b₂ b₁ a₁ c₁ 4 a₂ a₁ b₂ b₂ a₂ c₂ 5 a₂ a₂ b₂ b₃ a₂ c₂ 6 a₃ a₁ b₂b₄ a₃ c₁

TABLE 5 The base table in Table 4 in which the same upper bytes valuesare stored only once. A₁, C₁ A₂ B₁ B₂ C₂ . . . 1 a₁ a₁ b₁ b₁ c₁ 2 a₁ a₂b₁ b₂ c₃ 3 a₁ a₃ b₂ b₁ c₁ 4 a₂ a₁ b₂ b₂ c₂ 5 a₂ a₂ b₂ b₃ c₂ 6 a₃ a₁ b₂b₄ c₁

Another specific example is given herein, for example, a set of data:10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 are provided. In this case, aconventional way is to store the set of data: 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20 in a column of a base table, and an explicitsynopsis: 10-20 is created and stored in a separate table. Someembodiments of the present invention provides an alternative way forstoring the set of data: dividing each data of the set of data intofirst digit (i.e., upper bytes) of 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, andsecond digit (i.e., lower bytes) of 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0,which are stored in separate columns in a base table. In such way, noexplicit synopsis is created, but first digit may act as an implicitsynopsis imbedded in the base table. Further, a range summary for firstdigit [1, 2] may be created, essentially generating an explicit synopsisof the implicit synopsis.

Some embodiments of the present invention provide a hybrid method toleverage both implicit and explicit synopsis. For example, a set ofimplicit synopsizes are created first for a set of original data, andthen for each of the set of implicit synopsis (that is much smaller thanthe set of original data), an explicit synopsis is created.

FIG. 5 shows flowchart 500 depicting a first method according to thepresent invention. FIG. 6 shows program 600 for performing at least someof the method steps of flowchart 500. This method and associatedsoftware will now be discussed, over the course of the followingparagraphs, with extensive reference to FIG. 5 (for the method stepblocks) and FIG. 6 (for the software blocks).

Processing begins at step S555, where partition module (“mod”) 605partition projection of each data value of a set of data values into aplurality of data types. The date types include numerical or comparablebytes values, for example, an upper byte value and a lower byte value.In this example, unsorted dates, for example, including order date,invoice date, and shipping date, are considered. The order date, invoicedate, and shipping date, are each partitioned vertically into year,month, and day.

Vertical byte partitioning of data involves changing the representationof the data. For example, a date column can be stored in three columnsof shorter width such as year, month, day or a 32 bytes integer can bestored as four columns of 4 bytes. Furthermore, columns such as orderdate, invoice date, shipping date, and payment date are likely to behighly correlated on the year and month attributes. Therefore, a singleyear/month column can be used to represent three different columns.Therefore, by changing the representation of data, further compressionand effective synopsis data skipping can be realized. For example,consider order date, invoice date, shipping date and payment date as aset of correlated columns, if a record is ‘2011-1-2’, ‘2011-1-10’,‘2011-1-30’, ‘2011-2-10’, then this record goes to the year 2011 block(where a block is simply a chunk of data or a set of records).

Different partitioning techniques may be used for different types ofdata. For example, for date type, one way is to divide by day, month,and year, as done in this example. For larger strings or integer types,the partitioning methods depend on the data. Alternatively, theprojection of each data value is performed by transforming each datavalue by a custom formula including geospatial grid, and dividing thetransformed value into most significant digits and least significantdigit.

Processing proceeds to step S560, where store module 610 stores theplurality of data types in a set of separate columns in a base table. Inthis example, as discussed herein, each data value of order date,invoice date, and shipping date, is partitioned vertically into year,month, and day. Thus, each order date comprises three columns (i.e.,year, month, and day) in the base table, and each invoice date and eachshipping date comprise their corresponding three columns in the basetable.

Further, each column of the set of separate columns for the plurality ofdata types in the base table may be sorted, for example, by sellingprice, order date, and any other attributes.

Further, as mentioned briefly in step S555, a set of columns in the basetable may be grouped by identifying correlated values for the set ofcolumns, for example, finding correlation/similarity such as prefix,prost fix, or substrings. One example is given by considering two setsof data: set 1 including Jan. 1, 2000; Jan. 4, 2000; Jan. 10, 2000, andset 2 including Feb. 1, 2000; Feb. 4, 2000; Feb. 10, 2000. Each value ofthe two sets of data is divided into month, day and year that are storedin separate columns in a base table. As shown, the two sets of data havethe same year value (i.e., 2000). So, instead of having two differentcolumns for the year value for each set of data, the same year valuecolumn is shared by the two set of data, that is, the correlated valuesfor a set of columns is stored only once. Thereby redundantly storingsimilar common parts may be avoided, which is, essentially, a form oflossless compression across columns.

Alternatively the plurality of data types in the set of separate columnsmay be compressed. In this example, TPC-H results for 1 GB databasehaving about 6 million records are used for description. TPC-H is anindustry standard benchmark for analytical workloads (Note: the term“TPC-H” may be subject to trademark rights in various jurisdictionsthroughout the world and are used here only in reference to the productsor services properly denominated by the marks to the extent that suchtrademark rights may exist.) In the case where each data value in eachdate (e.g., order date, invoice date and shipping date) is notpartitioned into an upper bytes value and a lower bytes value, the sizeof each date column on average is 53 MB without data compression, and 13MB with data compression. However, in the case where each data value ineach date is partitioned vertically into an upper bytes value and alower bytes value (e.g., separated by year, month, and day), the size ofthe year column is 29 MB without data compression whereas 2.0 MB withdata compression; the size of the month column is 18 MB without datacompression whereas 3.5 MB with data compression; and the size of theday column is 18 MB without data compression whereas 4.9 MB with datacompression. Thus, the total individually compressed size is 10.4 MB(2.0 MB+3.5 MB+4.9 MB) which is less than 13 MB when not partitionedvertically. Further, with the three year columns grouped (i.e.,correlated but not sorted), the size of the grouped year column is 3.5MB that is less than 6.0 MB where each ungrouped year column take about2.0 MB.

Further, with the upper bytes values and the lower bytes values aresorted, the size of day column is 23 KB with data compression and 18 MBwithout data compression; the size of month column is 18 KB with datacompression and 18 MB without data compression; and the size of yearcolumn ranges from about 43 KB to about 94 KB with data compression and29 MB without data compression. The total individually compressed sizeis 84 KB (23 KB+18 KB+43 KB), which is less than 131 KB that is thecompressed size of a single column sorted and not partitionedvertically. Further, with the three year columns grouped (i.e.,correlated and sorted), the compressed size of the grouped year columnis 183 KB that is less than 217 KB (43 KB+80 KB+94 KB) where eachungrouped year column take about 43 KB, 80 KB, and 94 KB respectively.

Processing proceeds to step S565, where retrieve module 615 retrieve adata block of the base table, and the data block comprises a subset ofthe plurality of data types. The data block herein refers to a chunk ofdata or a set of records. In this example, the data block is a set ofrecords, comprising, such as, records 1-4 in Table 6. It should be notedthat a subset, as the term is used herein, may include each of the setfrom which it is taken, that is, a subset of the plurality of data typesmay include all of the data types.

TABLE 6 Show a data block comprising a set of records with a block IDYEAR ORDER MONTH ORDER DAY . . . 1 2000 02 01 Block 2 2000 02 02 ID: 001{open oversize brace} 3 2000 02 03 4 2000 02 04 5 2001 03 02 6 2001 0102

Processing proceeds to step S570, where predicate module 620 applies aplurality of predicates on the data block. The plurality of predicatescorresponds to the plurality of data types. For example, if a querylooks for a value that is 15, then a first predicate of the plurality ofpredicates may be applied on the upper byte type having a value that is“1,” and a second predicate of the plurality of predicates may beapplied on the lower byte type having a value that is “5.”

Processing proceeds to step S575, where predicate module 620 determinewhether or not the applied predicates pass the corresponding data types.In this example, if not passing, processing proceeds along the “no”branch to step S580 where the process skips the data block to end thequery. Alternatively, if not passing, processing proceeds back (notshown) to step S565 to retrieve another data block for the query. Inthis example, the applied predicates do pass the corresponding datatypes of the data block, so processing proceeds along the “yes” branchto step S585.

At step S585, block module 325 returns the data block. In this theexample, upon passing each predicate of the plurality of predicatesapplied on the corresponding data types, the data block is returned, forexample, a block ID is used for identifying the block of data as shownin Table 6 for further subsequent pipeline processing.

The embedded synopsis of multi-column range summaries are beneficialbecause of the lower update cost and fewer objects to maintain thatrequires less DBA (database administrator) involvement intuning/maintaining/replicating these objects. Some embodiments of thepresent invention may include one, or more, of the following features,characteristics and/or advantages: (i) efficient query processing (anddata skipping) using multi-column range summaries; (ii) improvement overconventional multi-column range summaries to efficiently support a lowselective query over unclustered data; (iii) bound and minimization ofthe replication factor in comparison with indexes and more accuratesynopsis; (iv) minimum update cost because indexes are no longer needed.

What is claimed is:
 1. A method for scanning and skipping data blocks,the method comprising: identifying a dataset for storage in a database,the dataset including a set of data values; partitioning the set of datavalues into a plurality of data bytes, the plurality of data bytes beingassociated with corresponding data values within the set of data;determining a set of first data bytes within the plurality of databytes, the set of first data bytes corresponding to a first data type;assigning a high order to the first data type; determining a set ofsecond data bytes within the plurality of data bytes, the set of seconddata bytes corresponding to a second data type; assigning a low order tothe second data type; storing the plurality of data bytes in a set ofcolumns, a high order column containing the first data type and the loworder column containing the second data type, the set of first databytes and the set of second data bytes maintaining an association withcorresponding data values, wherein the set of data values are stored inthe set of columns of the database; compressing the set of first databytes in the high order column; establishing a vertical partition of thehigh order column dividing the high order column into a plurality ofdata blocks, each data block having a corresponding data byte andcorresponding second data bytes in the low order column; responsive toreceiving a query of the dataset, applying a plurality of predicates onthe plurality of data blocks, the plurality of predicates correspondingto the plurality of data bytes; and searching only a subset of thesecond data type in the low order column, the subset corresponding to asubset of data blocks in the high order column meeting the plurality ofpredicates; wherein: at least the step of storing the plurality of datatypes is performed by computer software running on computer hardware. 2.The method of claim 1, further comprising: returning search resultsbased only on a search of data types passing each predicate of theplurality of predicates.
 3. The method of claim 1, wherein the step ofpartitioning the set of data values includes: transforming each datavalue into a multi-byte string corresponding to data types of ageospatial grid; and dividing the multi-byte string into the pluralityof data bytes.
 4. The method of claim 1, further comprising: identifyinga set of correlated values from the set of data values; partitioning theset of correlated values into a common data byte and a set ofcorresponding low order data bytes, the common data byte being foundwithin each correlated value; and storing the common data byte in thehigh order column and the low order data bytes in the low order column.5. The method of claim 4, wherein the set of correlated values includesa prefix, a postfix, and/or a set of substrings.
 6. The method of claim1, further comprising: sorting the set of first data bytes in the highorder column.
 7. A computer program product for scanning and skippingdata blocks, the computer program product comprising a computer readablestorage medium having stored thereon: first program instructionsprogrammed to identify a dataset for storage in a database, the datasetincluding a set of data values; second program instructions programmedto partition the set of data values into a plurality of data bytes, theplurality of data bytes being associated with corresponding data valueswithin the set of data values; third program instructions programmed todetermine a set of first data bytes within the plurality of data bytes,the set of first data bytes corresponding to a first data type; fourthprogram instructions programmed to assign a high order to the first datatype; fifth program instructions programmed to determine a set of seconddata bytes within the plurality of data bytes, the set of second databytes corresponding to a second data type; sixth program instructionsprogrammed to assign a low order to the second data type; seventhprogram instructions programmed to store the plurality of data bytes ina set of columns, a high order column containing the first data type andthe low order column containing the second data type, the set of firstdata bytes and the set of second data bytes maintaining an associationwith corresponding data values, wherein the set of data values arestored in the set of columns of the database; eighth programinstructions programmed to compress the set of first data bytes in thehigh order column; ninth program instructions programmed to establish avertical partition of the high order column dividing the high ordercolumn into a plurality of data blocks, each data block having acorresponding data byte and corresponding second data bytes in the loworder column; tenth program instructions programmed to, responsive toreceiving a query of the dataset, apply a plurality of predicates on theplurality of data blocks, the plurality of predicates corresponding tothe plurality of data bytes; and eleventh program instructionsprogrammed to search only a subset of the second data type in the loworder column, the subset corresponding to a subset of data blocks in thehigh order column meeting the plurality of predicates.
 8. The computerprogram product of claim 7, further comprising: twelfth programinstructions programmed to return search results based only on a searchof data types passing each predicate of the plurality of predicates. 9.The computer program product of claim 7, further comprising: twelfthprogram instructions programmed to identify a set of correlated valuesfrom the set of data values; thirteenth program instructions programmedto partition the set of correlated values into a common data byte and aset of corresponding low order data bytes, the common data byte beingfound within each correlated value; and fourteenth program instructionsprogrammed to store the common data byte in the high order column andthe low order data bytes in the low order column.
 10. The computerprogram product of claim 9, wherein the set of correlated valuesincludes a prefix, a postfix, and/or a set of substrings.
 11. A computersystem for scanning and skipping data blocks, the computer systemcomprising: a processor(s) set; and a computer readable storage medium;wherein: the processor set is structured, located, connected, and/orprogrammed to run program instructions stored on the computer readablestorage medium; and the program instructions include: first programinstructions programmed to identify a dataset for storage in a database,the dataset including a set of data values; second program instructionsprogrammed to partition the set of data values into a plurality of databytes, the plurality of data bytes being associated with correspondingdata values within the set of data values; third program instructionsprogrammed to determine a set of first data bytes within the pluralityof data bytes, the set of first data bytes corresponding to a first datatype; fourth program instructions programmed to assign a high order tothe first data type; fifth program instructions programmed to determinea set of second data bytes within the plurality of data bytes, the setof second data bytes corresponding to a second data type; sixth programinstructions programmed to assign a low order to the second data type;seventh program instructions programmed to store the plurality of databytes in a set of columns, a high order column containing the first datatype and the low order column containing the second data type, the setof first data bytes and the set of second data bytes maintaining anassociation with corresponding data values, wherein the set of datavalues are stored in the set of columns of the database; eighth programinstructions programmed to compress the set of first data bytes in thehigh order column; ninth program instructions programmed to establish avertical partition of the high order column dividing the high ordercolumn into a plurality of data blocks, each data block having acorresponding data byte and corresponding second data bytes in the loworder column; tenth program instructions programmed to, responsive toreceiving a query of the dataset, apply a plurality of predicates on theplurality of data blocks, the plurality of predicates corresponding tothe plurality of data bytes; and eleventh program instructionsprogrammed to search only a subset of the second data type in the loworder column, the subset corresponding to a subset of data blocks in thehigh order column meeting the plurality of predicates.
 12. The computersystem of claim 11, further comprising: twelfth program instructionsprogrammed to return search results based only on a search of data typespassing each predicate of the plurality of predicates.
 13. The computersystem of claim 11, further comprising: twelfth program instructionsprogrammed to sort the set of first data bytes in the high order column.14. The computer system of claim 11, further comprising: twelfth programinstructions programmed to identify a set of correlated values from theset of data values; thirteenth program instructions programmed topartition the set of correlated values into a common data byte and a setof corresponding low order data bytes, the common data byte being foundwithin each correlated value; and fourteenth program instructionsprogrammed to store the common data byte in the high order column andthe low order data bytes in the low order column.
 15. The computersystem of claim 14, wherein the set of correlated values includes aprefix, a postfix, and/or a set of substrings.
 16. The computer programproduct of claim 7, wherein the second program instructions programmedto partition the set of data values includes: program instructionsprogrammed to transform each data value into a multi-byte stringcorresponding to data types of a geospatial grid; and programinstructions programmed to divide the multi-byte string into theplurality of data bytes.
 17. The computer system of claim 11, whereinthe second program instructions programmed to partition the set of datavalues includes: program instructions programmed to transform each datavalue into a multi-byte string corresponding to data types of ageospatial grid; and program instructions programmed to divide themulti-byte string into the plurality of data bytes.
 18. The computerprogram product of claim 7, further comprising: twelfth programinstructions programmed to sort the set of first data bytes in the highorder column.